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Merge pull request #257 from Gilead-BioStats/release-v0.2.0
Release v0.2.0
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| #' Poisson Analysis - Predicted Boundaries | ||
| #' | ||
| #' @details | ||
| #' | ||
| #' Fits a Poisson model to site level data and then calculates predicted count values and upper- and lower- bounds for across the full range of exposure values. | ||
| #' | ||
| #' @section Statistical Methods: | ||
| #' | ||
| #' This function fits a poisson model to site-level data and then calculates residuals for each site. The poisson model is run using standard methods in the `stats` package by fitting a `glm` model with family set to `poisson` using a "log" link. Upper and lower boundary values are then calculated using the method described here TODO: Add link. In short, | ||
| #' | ||
| #' @section Data Specification: | ||
| #' | ||
| #' The input data (` dfTransformed`) for the Analyze_Poisson is typically created using \code{\link{Transform_EventCount}} and should be one record per Site with columns for: | ||
| #' - `SubjectID` - Unique subject ID | ||
| #' - `SiteID` - Site ID | ||
| #' - `TotalCount` - Number of Events | ||
| #' - `TotalExposure` - Number of days of exposure | ||
| #' | ||
| #' @param dfTransformed data.frame in format produced by \code{\link{Transform_EventCount}}. Must include SubjectID, SiteID, TotalCount and TotalExposure. | ||
| #' @param vThreshold upper and lower boundaries in residual space. Should be identical to the threhsolds used AE_Assess(). | ||
| #' | ||
| #' @importFrom stats glm offset poisson pnorm | ||
| #' @importFrom broom augment | ||
| #' @importFrom lamW lambertW0 lambertWm1 | ||
| #' | ||
| #' @return data frame containing predicted boundary values with upper and lower bounds across the range of observed values | ||
| #' | ||
| #' @examples | ||
| #' dfInput <- AE_Map_Adam( safetyData::adam_adsl, safetyData::adam_adae ) | ||
| #' dfTransformed <- Transform_EventCount( dfInput, strCountCol = 'Count', strExposureCol = "Exposure" ) | ||
| #' dfBounds <- Analyze_Poisson_PredictBounds(dfTransformed, c(-5,5)) | ||
| #' | ||
| #' @export | ||
| Analyze_Poisson_PredictBounds <- function( dfTransformed, vThreshold=c(-5,5)){ | ||
| dfTransformed$LogExposure <- log(dfTransformed$TotalExposure) | ||
| cModel <- glm( | ||
| TotalCount ~ stats::offset(LogExposure), | ||
| family=poisson(link="log"), | ||
| data=dfTransformed | ||
| ) | ||
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| dfBounds <- data.frame( | ||
| LogExposure = seq( | ||
| min(dfTransformed$LogExposure)-0.05, | ||
| max(dfTransformed$LogExposure)+0.05, | ||
| by=0.05 | ||
| )) %>% | ||
| mutate( vMu = as.numeric( exp( .data$LogExposure * cModel$coefficients[2] + cModel$coefficients[1] ))) %>% | ||
| mutate( vWHi = (vThreshold[2]^2 - 2 * .data$vMu) / ( 2 * exp(1) * .data$vMu )) %>% | ||
| mutate( vWLo = (vThreshold[1]^2 - 2 * .data$vMu) / ( 2 * exp(1) * .data$vMu )) %>% | ||
| mutate( PredictYHigh = ( vThreshold[2]^2-2* .data$vMu) / (2*lamW::lambertW0( .data$vWHi ))) %>% | ||
| mutate( PredictYLo = ( vThreshold[1]^2-2* .data$vMu) / (2*lamW::lambertWm1( .data$vWLo ))) %>% | ||
| mutate( MeanCount = exp( .data$LogExposure * cModel$coefficients[2] + cModel$coefficients[1])) %>% | ||
| mutate( LowerCount = if_else(is.nan( .data$PredictYLo ), 0, .data$PredictYLo )) %>% | ||
| mutate( UpperCount = if_else(is.nan( .data$PredictYHigh ), 0, .data$PredictYHigh )) %>% | ||
| select( .data$LogExposure, .data$MeanCount, .data$LowerCount, .data$UpperCount ) | ||
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| return( dfBounds ) | ||
| } |
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